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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,7,31]]},"abstract":"<jats:p>\n            The smallest semantic unit of the Burmese language is called the syllable. In the present study, it is intended to propose the first neural joint learning model for Burmese syllable segmentation, word segmentation, and\n            <jats:bold>part-of-speech<\/jats:bold>\n            (\n            <jats:bold>POS<\/jats:bold>\n            ) tagging with the BERT. The proposed model alleviates the error propagation problem of the syllable segmentation. More specifically, it extends the neural joint model for Vietnamese word segmentation, POS tagging, and dependency parsing [28] with the pre-training method of the Burmese character, syllable, and word embedding with BiLSTM-CRF-based neural layers. In order to evaluate the performance of the proposed model, experiments are carried out on Burmese benchmark datasets, and we fine-tune the model of multilingual BERT. Obtained results show that the proposed joint model can result in an excellent performance.\n          <\/jats:p>","DOI":"10.1145\/3436818","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T14:10:16Z","timestamp":1622038216000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Neural Joint Model with BERT for Burmese Syllable Segmentation, Word Segmentation, and POS Tagging"],"prefix":"10.1145","volume":"20","author":[{"given":"Cunli","family":"Mao","sequence":"first","affiliation":[{"name":"Key Laboratory of Artificial Intelligence, Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China"}]},{"given":"Zhibo","family":"Man","sequence":"additional","affiliation":[{"name":"Key Laboratory of Artificial Intelligence, Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4012-461X","authenticated-orcid":false,"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Artificial Intelligence, Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China"}]},{"given":"Shengxiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Artificial Intelligence, Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China"}]},{"given":"Zhenhan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Artificial Intelligence, Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China"}]},{"given":"Hongbin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Artificial Intelligence, Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China"}]}],"member":"320","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Chris Alberti Kenton Lee and Michael Collins. 2019. 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